mlr_learners_surv.dnnsurv | R Documentation |
Fits a neural network based on pseudo-conditional survival probabilities.
Calls survivalmodels::dnnsurv()
from package 'survivalmodels'.
Custom nets can be used in this learner either using the
survivalmodels::build_keras_net utility function or using keras.
The number of output channels should be of length 1
and number of input channels is
the number of features plus number of cuts.
This Learner can be instantiated via the dictionary mlr_learners or with the associated sugar function lrn()
:
mlr_learners$get("surv.dnnsurv") lrn("surv.dnnsurv")
Task type: “surv”
Predict Types: “crank”, “distr”
Feature Types: “integer”, “numeric”
Required Packages: mlr3, mlr3proba, mlr3extralearners, survivalmodels, keras, pseudo, tensorflow, distr6
Id | Type | Default | Levels | Range |
cuts | integer | 5 | [1, \infty) |
|
cutpoints | untyped | - | - | |
custom_model | untyped | - | - | |
optimizer | character | adam | adadelta, adagrad, adamax, adam, nadam, rmsprop, sgd | - |
lr | numeric | 0.02 | [0, \infty) |
|
beta_1 | numeric | 0.9 | [0, 1] |
|
beta_2 | numeric | 0.999 | [0, 1] |
|
epsilon | numeric | - | [0, \infty) |
|
decay | numeric | 0 | [0, \infty) |
|
clipnorm | numeric | - | (-\infty, \infty) |
|
clipvalue | numeric | - | (-\infty, \infty) |
|
momentum | numeric | 0 | [0, \infty) |
|
nesterov | logical | FALSE | TRUE, FALSE | - |
loss_weights | untyped | - | - | |
weighted_metrics | untyped | - | - | |
early_stopping | logical | FALSE | TRUE, FALSE | - |
min_delta | numeric | 0 | [0, \infty) |
|
patience | integer | 0 | [0, \infty) |
|
verbose | integer | 0 | [0, 2] |
|
baseline | numeric | - | (-\infty, \infty) |
|
restore_best_weights | logical | FALSE | TRUE, FALSE | - |
batch_size | integer | 32 | [1, \infty) |
|
epochs | integer | 10 | [1, \infty) |
|
validation_split | numeric | 0 | [0, 1] |
|
shuffle | logical | TRUE | TRUE, FALSE | - |
sample_weight | untyped | - | - | |
initial_epoch | integer | 0 | [0, \infty) |
|
steps_per_epoch | integer | - | [1, \infty) |
|
validation_steps | integer | - | [1, \infty) |
|
steps | integer | - | [0, \infty) |
|
callbacks | untyped | - | - | |
rho | numeric | 0.95 | (-\infty, \infty) |
|
global_clipnorm | numeric | - | (-\infty, \infty) |
|
use_ema | logical | - | TRUE, FALSE | - |
ema_momentum | numeric | 0.99 | (-\infty, \infty) |
|
ema_overwrite_frequency | numeric | - | (-\infty, \infty) |
|
jit_compile | logical | TRUE | TRUE, FALSE | - |
initial_accumultator_value | numeric | 0.1 | (-\infty, \infty) |
|
amsgrad | logical | FALSE | TRUE, FALSE | - |
lr_power | numeric | -0.5 | (-\infty, \infty) |
|
l1_regularization_strength | numeric | 0 | [0, \infty) |
|
l2_regularization_strength | numeric | 0 | [0, \infty) |
|
l2_shrinkage_regularization_strength | numeric | 0 | [0, \infty) |
|
beta | numeric | 0 | (-\infty, \infty) |
|
centered | logical | FALSE | TRUE, FALSE | - |
Package 'survivalmodels' is not on CRAN and has to be install from GitHub via
remotes::install_github("RaphaelS1/survivalmodels")
.
verbose
is initialized to 0.
mlr3::Learner
-> mlr3proba::LearnerSurv
-> LearnerSurvDNNSurv
new()
Creates a new instance of this R6 class.
LearnerSurvDNNSurv$new()
clone()
The objects of this class are cloneable with this method.
LearnerSurvDNNSurv$clone(deep = FALSE)
deep
Whether to make a deep clone.
RaphaelS1
Zhao, Lili, Feng, Dai (2019). “Dnnsurv: Deep neural networks for survival analysis using pseudo values.” arXiv preprint arXiv:1908.02337.
Dictionary of Learners: mlr3::mlr_learners.
as.data.table(mlr_learners)
for a table of available Learners in the running session (depending on the loaded packages).
Chapter in the mlr3book: https://mlr3book.mlr-org.com/basics.html#learners
mlr3learners for a selection of recommended learners.
mlr3cluster for unsupervised clustering learners.
mlr3pipelines to combine learners with pre- and postprocessing steps.
mlr3tuning for tuning of hyperparameters, mlr3tuningspaces for established default tuning spaces.
learner = mlr3::lrn("surv.dnnsurv")
print(learner)
# available parameters:
learner$param_set$ids()
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